A trajectory pattern refers to data describing the characteristics of trajectories between given locations or in a certain shape. For example, given two points on the map, a trajectory pattern may indicate the route segments that are frequently passed by the trajectories between those two points. The trajectory patterns are widely used in many map-based applications such as carpooling in the industry of transport/social-network, route monitoring and recommendation in logistics industry, driving risk assessment in insurance industry, and the like.
Manual identification of trajectory patterns is a time consuming and error-prone process with relatively low precision. In order to automate the identification of trajectory patterns, it is necessary to process the huge amount of trajectory data. In general, a trajectory is determined based on the data acquired by mobile sensors as Global Positioning System (GPS) receivers. In many applications, the mobile sensors generate continuous data and stream the data to backend servers at very high sampling rates. The data streams featured with intensive spatial and/or temporal trajectory information require considerable computation resource for subsequent data processing and analysis such as the pattern mining. It is found that the massive sampling data leads to intolerable processing time in the processes such as clustering of trajectories. In addition, the huge amount of data representing the trajectories data is a bottleneck in many other applications.